The semiconductor lowered the cost of arithmetic, which the Harvard Business Review cites to predict that the economics of machine learning mean we’ll start using predictions on everything: “The first effect of machine intelligence will be to lower the cost of goods and services that rely on prediction.” The next step will be that we’ll start using prediction as an input even in industries where it has typically had little reason to be applied.

That shift is already underway as big tech companies change their business models to incorporate AI at every level. Google, Facebook, Twitter, Amazon, and Microsoft are all investing heavily; they’ve even worked together to establish best practices in AI ethics, a partnership from which Apple was notably marked absent.

Google announced that their translation team discovered something interesting: after they turned over translation to a machine learning-enabled AI, it figured out its own way of translating.

After being taught to translate between English and Korean, then between Japanese and English, the AI invented its own fourth reference language that may draw upon deep-learning insights into a shared structure between the three languages. “This “interlingua” seems to exist as a deeper level of representation that sees similarities between a sentence or word in all three languages,” says TechCrunch.

Full explanation of the phenomenon from the Google team here.

Machine learning treats a dataset as a closed system. In the case of comic books, too much happens in between and outside the frame for the machine to understand.

For now, only humans can achieve “closure,” a measure of literary comprehension that computer scientists test in humans and AI by asking for a prediction of text in successive panels.

A New Zealand scientist, apparently fed up with the blindly financial reasons why academic conferences often accept papers of dubious quality, wrote a paper entirely reliant on Autocorrect’s AI. The paper was accepted.